• DocumentCode
    3393134
  • Title

    On damage monitoring in historical buildings via Neural Networks

  • Author

    Carnimeo, Leonarda ; Foti, Dora ; Vacca, Vitantonio

  • Author_Institution
    Dept. of Electr. & Inf. Eng., Tech. Univ. of Bari, Bari, Italy
  • fYear
    2015
  • fDate
    9-10 July 2015
  • Firstpage
    157
  • Lastpage
    161
  • Abstract
    It is well known that ancient buildings suffer a high vulnerability to hazards, which may induce unpredictable damages. For this purpose, a main objective to be pursued concerns with the development of techniques for monitoring historical buildings and immediately alerting in case of early vulnerability warnings. This paper proposes a noninvasive Neural Network-based (NN-based) approach for Monitoring heritage buildings providing alerts in risk events. More in detail, a neural approach is suggested with the aim of predicting early warnings of risk events by detecting time novelties in images of historical evidences.
  • Keywords
    buildings (structures); condition monitoring; hazards; history; neural nets; risk analysis; structural engineering computing; NN; ancient buildings; damage monitoring; early vulnerability warnings; hazard vulnerability; heritage buildings; historical buildings; historical evidences; noninvasive neural network-based approach; risk events; Artificial neural networks; Biological neural networks; Buildings; Cameras; Monitoring; Neurons; Training; Risk prevention; historical buildings; image processing techniques; neural networks; sensor-based monitoring systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Environmental, Energy and Structural Monitoring Systems (EESMS), 2015 IEEE Workshop on
  • Conference_Location
    Trento
  • Print_ISBN
    978-1-4799-8214-1
  • Type

    conf

  • DOI
    10.1109/EESMS.2015.7175870
  • Filename
    7175870